P25 Cognitive Differential Diagnosis of Hepatic Encephalopathy in a Cohort of Outpatients With Chronic Liver Disease
Bibliographic record
Abstract
Background: Covert hepatic encephalopathy (CHE) is a complex and multifactorial complication of chronic liver diseases (CLD). Other etiologies may cause neurocognitive impairment (NI) independently form the liver condition, making the differential diagnosis difficult using cognitive tests. The aim of this study was to search for cognitive tools enabling to identify patients with CHE, taking into account the presence of other sources of NI. Methods: Retrospective analysis of a prospective cohort of patients with CLD (March 2018-November 2022) referred to our outpatient clinics for suspicion of CHE. Multimodal work-up was performed in our multidisciplinary team: hepatologists, neurologist, neuropsychologist, biomarkers, electroencephalogram (EEG) and brain MRI (MRI). An adjudication committee involving the aforementioned physicians made the diagnosis of CHE. Results: 164 patients were included: 77% cirrhosis (alcohol/MASH/virus in 62/54/16%), 23% portosinusoïdal vascular liver disease. 70% had a previous history of HE, among them 98% had ammonia-lowering medications. Overall, 63% patients were diagnosed with CHE, 37% with other causes of NI, 26% with no NI and 26% with mix causes of NI. Only 37% of them had CHE without other causes of NI. Age, prevalence of cirrhosis, cardiovascular risk factors, and alcohol abuse were similar between CHE and non-CHE groups. BMI and MASH patients were higher in the CHE group (respectively p = 0.048 and p =0.001). MoCA test (P=0.049), PHES (P=0.003), ANT (P=0.001), reflexive praxis (P=0.042), verbal digit span (P=0.023) and of Rey’s Figure copy (P=0.006) were significantly lower in CHE patients. Among patients with exposure to neurological risk factors, 70 % were diagnosed with CHE. Only the ANT (P=0.032) and the verbal digit span (P=0.005) were found significantly worse in these patients. When taking into account the presence of neurological comorbidities, the assessment of memory enabled to separate patients with neurological illnesses from patients without, independently from HE diagnosis. Conclusion: The diagnosis of HE based on NI is complicated. Validated tests (PHES, ANT) can be sensitive for HE but for other neurological damage as well, they are also found to be worse in patients with mix causes of NI. The assessment of memory seems to be an important factor for differential diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".